Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Localization of acoustic sources using a decentralized particle filter | Xaver et al. EURASIP Journal on Wireless Communications and Networking 2011 2011 94 http content 2011 1 94 o EURASIP Journal on Wireless Communications and Networking a SpringerOpen Journal RESEARCH Open Access Localization of acoustic sources using a decentralized particle filter 1 12 1 Florian Xaver Gerald Matz Peter Gerstoft and Christoph Mecklenbrauker Abstract This paper addresses the decentralized localization of an acoustic source in a wireless sensor network based on the underlying partial differential equation PDE . The PDE is transformed into a distributed state-space model and augmented by a source model. Inferring the source state amounts to a non-linear non-Gaussian Bayesian estimation problem for whose solution we implement a decentralized particle filter PF operating within and across clusters of sensor nodes. The aggregation of the local posterior distributions from all clusters is achieved via an enhanced version of the maximum consensus algorithm. Numerical simulations illustrate the performance of our scheme. Keywords source localization acoustic wave equation distributed state-space model sequential Bayesian estimation decentralized particle filter argumentum-maximi consensus algorithm I Introduction Background and state of the art In this paper we use a physics-based model and a Bayesian approach to develop a decentralized particle filter PF for acoustic source localization in a sensor network SN . In a decentralized PF the processing is done locally at the sensors without using a fusion center. Thereby the estimated position is known at every sensor in consequence of this decentralized process. The problem formulation in this paper is motivated by indoor localization of an acoustic source. A hallway is modeled including basic boundary conditions for windows membranes and walls. The source localization problem has been studied . in 1-3 4 p. 4089 ff 5 p. 746 ff and 6 all of which use a sequential Bayesian estimator 7
